CheckM reference markers for Prokaryotes

Description

List of Universal Single Copy Genes for Bacteria and Archaea.

Usage

data(CheckMProkaryote)

Format

List containing vectors of PFAMs, each vector corresponding to a different set of collocated markers.

References

Parks, Imelfort, Skennerton, Hugenholtz & Tyson (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes Genome Res. 25:1043-1055. (PubMed).

See Also

USiCGs, MGOGs and MGKOs for an alternative set of single copy genes, and for examples on how to generate copy numbers.

Hadza hunter-gatherer gut metagenomes

Description

Subset of two bins (and the associated contigs and genes) generated by running SqueezeMeta on two gut metagenomic samples obtained from two hunter-gatherers of the Hadza ethnic group.

Usage

data(Hadza)

Format

A SQM object; see loadSQM.

Source

SRR1927149, SRR1929485.

References

Rampelli et al., 2015. Metagenome Sequencing of the Hadza Hunter-Gatherer Gut Microbiota. Curr. biol. 25:1682-93 (PubMed).

Examples

data(Hadza)
plotTaxonomy(Hadza, "genus", rescale=TRUE)
plotFunctions(Hadza, "COG")

Single Copy Phylogenetic Marker Genes from Sunagawa's group (KOs)

Description

Lists of Single Copy Phylogenetic Marker Genes. These are useful for transforming coverages or tpms into copy numbers. This is an alternative way of normalizing data in order to be able to compare functional profiles in samples with different sequencing depths.

Usage

data(MGKOs)

Format

Character vector with the KEGG identifiers for 10 Single Copy Phylogenetic Marker Genes.

References

Salazar, G et al. (2019). Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome Cell 179:1068-1083. (PubMed).

See Also

MGOGs for an equivalent list using OGs instead of KOs; USiCGs for an alternative set of single copy genes, and for examples on how to generate copy numbers.

Single Copy Phylogenetic Marker Genes from Sunagawa's group (OGs)

Description

Lists of Single Copy Phylogenetic Marker Genes. These are useful for transforming coverages or tpms into copy numbers. This is an alternative way of normalizing data in order to be able to compare functional profiles in samples with different sequencing depths.

Usage

data(MGOGs)

Format

Character vector with the COG identifiers for 10 Single Copy Phylogenetic Marker Genes.

References

Salazar, G et al. (2019). Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome Cell 179:1068-1083. (PubMed).

See Also

MGKOs for an equivalent list using KOs instead of OGs; USiCGs for an alternative set of single copy genes, and for examples on how to generate copy numbers.

RecA/RadA recombinase

Description

The recombination protein RecA/RadA is essential for the repair and maintenance of DNA, and has homologs in every bacteria and archaea. By dividing the coverage of functions by the coverage of RecA, abundances can be transformed into copy numbers, which can be used to compare functional profiles in samples with different sequencing depths. RecA-derived copy numbers are available in the SQM object (SQM$functions$<annotation_type>$copy_number).

Usage

data(RecA)

Format

Character vector with the COG identifier for RecA/RadA.

Source

EggNOG Database.

Examples

data(Hadza)
data(RecA)
### Let's calculate the average copy number of each function in our samples.
# We do it for COG annotations here, but we could also do it for KEGG or PFAMs.
COG.coverage = Hadza$functions$COG$cov
COG.copynumber = t(t(COG.coverage) / COG.coverage[RecA,]) # Sample-wise division by RecA coverage.

Convert a SQM object into a microtable object from the microeco package

Description

This function will convert the selected features from a SQM object into an object of the microtable class from the microeco package. When possible, it will also include the taxonomy of the included features (for functional classifications, the taxonomy table will instead include the description of each feature ID). Optionally, it accepts a meta table that will be passed as provided to microtable$new.

Usage

SQM_to_microeco(
  SQM,
  features = "genus",
  count = "abund",
  md = NULL,
  nocds = "treat_separately",
  no_partial_classifications = FALSE,
  ignore_unclassified = FALSE,
  ignore_unmapped = FALSE,
  bin_tax_source = "SQM",
  include_seqs = FALSE
)

Arguments

Value

A microtable.

See Also

SQM_to_phyloseq for exporting a SQM/SQMlite/SQM object as a phyloseq object.

Convert a SQM object into a phyloseq object from the phyloseq package

Description

This function will convert the selected features from a SQM object into a phyloseq object from the phyloseq package. When possible, it will also include the taxonomy of the included features (for functional classifications, the taxonomy table will instead include the description of each feature ID). Optionally, it accepts a meta table that will be passed as provided to the phyloseq object constructor.

Usage

SQM_to_phyloseq(
  SQM,
  features = "genus",
  count = "abund",
  md = NULL,
  nocds = "treat_separately",
  no_partial_classifications = FALSE,
  ignore_unclassified = FALSE,
  ignore_unmapped = FALSE,
  bin_tax_source = "SQM",
  include_seqs = FALSE
)

Arguments

Value

A phyloseq object.

See Also

SQM_to_microeco for exporting a SQM/SQMlite/SQM object as a microtable object.

Universal Single-Copy Genes

Description

Lists of Universal Single Copy Genes for Bacteria and Archaea. These are useful for transforming coverages or tpms into copy numbers. This is an alternative way of normalizing data in order to be able to compare functional profiles in samples with different sequencing depths.

Usage

data(USiCGs)

Format

Character vector with the KEGG identifiers for 15 Universal Single Copy Genes.

Source

Carr et al., 2013. Table S1.

References

Carr, Shen-Orr & Borenstein (2013). Reconstructing the Genomic Content of Microbiome Taxa through Shotgun Metagenomic Deconvolution PLoS Comput. Biol. 9:e1003292. (PubMed).

See Also

MGOGs and MGKOs for an alternative set of single copy genes, and for examples on how to generate copy numbers.

Examples

data(Hadza)
data(USiCGs)
### Let's look at the Universal Single Copy Gene distribution in our samples.
KEGG.tpm = Hadza$functions$KEGG$tpm
all(USiCGs %in% rownames(KEGG.tpm)) # Are all the USiCGs present in our dataset?
# Plot a boxplot of USiCGs tpms and calculate median USiCGs tpm.
# This looks weird in the test dataset because it contains only a small subset of the metagenomes.
# In a set of complete metagenomes USiCGs should have fairly similar TPM averages
# and low dispersion across samples.
boxplot(t(KEGG.tpm[USiCGs,]), names=USiCGs, ylab="TPM", col="slateblue2")
 
### Now let's calculate the average copy numbers of each function.
# We do it for KEGG annotations here, but we could also do it for COGs or PFAMs.
USiCGs.cov = apply(Hadza$functions$KEGG$cov[USiCGs,], 2, median)
# Sample-wise division by the median USiCG coverage.
KEGG.copynumber = t(t(Hadza$functions$KEGG$cov) / USiCGs.cov)

Combine several SQM objects

Description

Combine an arbitrary number of SQM objects into a single SQM object (if the input objects contain the same samples, i.e. they come from the same SqueezeMeta run) or a single SQMbunch object. For combining results from sqm_reads.pl or sqm_longreads.pl please check combineSQMlite. The parameters below (other than ...) will take only effect if the input objects contain the same samples. Otherwise the input objects will be taken as they are, with no recalculation of taxonomy, function or rescaling,

Usage

combineSQM(
  ...,
  tax_source = "orfs",
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE,
  recalculate_bin_stats = TRUE
)

Arguments

Value

A SQM or SQMbunch object

See Also

subsetFun, subsetTax, combineSQMlite

Examples

data(Hadza)
# Select Carbohydrate metabolism ORFs in Bacteroidota,
#  and Amino acid metabolism ORFs in Proteobacteria
bact = subsetTax(Hadza, "phylum", "Bacteroidota")
bact.carb = subsetFun(bact, "Carbohydrate metabolism")
baci = subsetTax(Hadza, "phylum", "Bacillota")
baci.amins = subsetFun(baci, "Amino acid metabolism")
bact.carb_proteo.amins = combineSQM(bact.carb, baci.amins, rescale_copy_number=FALSE)

Combine several SQM or SQMlite objects

Description

Combine an arbitrary number of SQM or SQMlite objects into a single SQMlite object. This function accepts objects originating from different projects (i.e. different SqueezeMeta runs).

Usage

combineSQMlite(...)

Arguments

Value

A SQMlite object

See Also

combineSQM

Examples

## Not run: 
data(Hadza)
# Load data coming from a different run
other = loadSQMlite("/path/to/other/project/tables") # e.g. if the project was run using sqm_reads
# (We could also use loadSQM to load the data as long as the data comes from a SqueezeMeta run)
combined = combineSQMlite(Hadza, other)
# Now we can plot together the samples from Hadza and the second project
plotTaxonomy(combined, 'family')

## End(Not run)

Create a bin from a vector of contigs

Description

Create a bin from a vector of contigs

Usage

create_bin(SQM, bin, contigs, delete_overlapping_bins = FALSE)

Arguments

Value

SQM object with the new binning information, including recalculated bin statistics if possible.

See Also

find_redundant_contigs, remove_contigs_from_bin

Export the bins of a SQM object

Description

Export the bins of a SQM object

Usage

exportBins(SQM, output_dir = "")

Arguments

Export the contigs of a SQM object

Description

Export the contigs of a SQM object

Usage

exportContigs(SQM, output_name = "")

Arguments

Export the taxonomy of a SQM object into a Krona Chart

Description

Generate a krona chart containing the full taxonomy from a SQM object.

Usage

exportKrona(SQM, output_name = NA)

Arguments

Details

Original code was kindly provided by Giuseppe D'Auria (dauria_giu@gva.es).

Value

No return value, but a krona chart is produced in the current working directory.

See Also

plotTaxonomy for plotting the most abundant taxa of a SQM object.

Examples

data(Hadza)
# Check that kronatools is present.
ecode = system('ktImportText', ignore.stdout = TRUE, ignore.stderr = TRUE)
# If so, run.
if(ecode==0) { exportKrona(Hadza, output_name = file.path(tempdir(), "krona.html")) }

Export the ORFs of a SQM object

Description

Export the ORFs of a SQM object

Usage

exportORFs(SQM, output_name = "")

Arguments

Export the functions of a SQM object into KEGG pathway maps

Description

This function is a wrapper for the pathview package (Luo et al., 2017. Nucleic acids research, 45:W501-W508). It will generate annotated KEGG pathway maps showing which reactions are present in the different samples. It will also generate legends with the color scales for each sample in separate png files.

Usage

exportPathway(
  SQM,
  pathway_id,
  count = "copy_number",
  samples = NULL,
  split_samples = FALSE,
  sample_colors = NULL,
  log_scale = FALSE,
  fold_change_groups = NULL,
  fold_change_colors = NULL,
  max_scale_value = NULL,
  color_bins = 10,
  rescale_percent = FALSE,
  output_dir = ".",
  output_suffix = "pathview"
)

Arguments

Value

A ggplot if split_samples = FALSE and the ggpattern package is installed, otherwise nothing. Additionally, Pathview figures will be written in the directory specified by output_dir.

See Also

plotFunctions for plotting the most functions taxa of a SQM object.

Examples

data(Hadza)

exportPathway(Hadza, "00910", count = 'copy_number',
              output_dir = tempdir(),
              output_suffix = "nitrogen_metabolism",
              sample_colors = c("red", "blue"))
exportPathway(Hadza, "00250", count = 'tpm',
              output_dir = tempdir(),
              output_suffix = "ala_asp_glu_metabolism_FoldChange", 
              fold_change_groups = list(c("H1"), c("H12")), max_scale_value=2)

Export results in tabular format

Description

This function is a wrapper for R's write.table function.

Usage

exportTable(table, output_name)

Arguments

Examples

data(Hadza)
Hadza.iron = subsetFun(Hadza, "iron")
# Write the taxonomic distribution at the genus level of all the genes related to iron.
exportTable(Hadza.iron$taxa$genus$percent, file.path(tempdir(), "Hadza.ironGenes.genus.tsv"))
# Now write the distribution of the different iron-related COGs
#  (Clusters of Orthologous Groups) across samples.
exportTable(Hadza.iron$functions$COG$tpm, file.path(tempdir(), "Hadza.ironGenes.COG.tsv"))
# Now write all the information contained in the ORF table.
exportTable(Hadza.iron$orfs$table, file.path(tempdir(), "Hadza.ironGenes.orftable.tsv"))

Find redundant contigs within a bin

Description

Find contigs with overlapping marker genes in a given bin, and suggest contigs to be removed in order to reduce contamination without affecting completeness. Note that this can give a quick idea of the contigs that are sources of contamination within a bin, but is not a replacement for proper bin refininement with other tools such as anvi\'o.

Usage

find_redundant_contigs(SQM, bin, minimum_overlap_for_removal = 1)

Arguments

Value

A character vector with the contigs deemed to be redundant. A heatmap showing how marker genes overlap over different contigs will also be produced.

See Also

create_bin, remove_contigs_from_bin

Examples

data(Hadza)
bin_name = "Hadza2merged.concoct.28.fa.contigs"
# Get redundant contigs that could be removed from our bin
candidates_for_removal = find_redundant_contigs(Hadza, bin_name)
# We can now remove them from the bin
Hadza.new.1 = remove_contigs_from_bin(Hadza, bin_name, candidates_for_removal)
# Or we can create a new bin out of them
#  which will also remove them from the original bin
Hadza.new.2 = create_bin(Hadza, "new_bin_name", candidates_for_removal)

Load a SqueezeMeta project into R

Description

This function takes the path to a project directory generated by SqueezeMeta (whose name is specified in the -p parameter of the SqueezeMeta.pl script) and parses the results into a SQM object. Alternatively, it can load the project data from a zip file produced by sqm2zip.py.

Usage

loadSQM(
  project_path,
  tax_mode = "prokfilter",
  trusted_functions_only = FALSE,
  single_copy_genes = "MGOGs",
  load_sequences = TRUE,
  engine = "data.table"
)

Arguments

Value

SQM object containing the parsed project. If more than one path is provided in project_path this function will return a SQMbunch object instead. The structure of this object is similar to that of a SQMlite object (see loadSQMlite) but with an extra entry named projects that contains one SQM object for input project. SQM and SQMbunch objects will otherwise behave similarly when used with the subset and plot functions from this package.

Prerequisites

Run SqueezeMeta! An example call for running it would be:

/path/to/SqueezeMeta/scripts/SqueezeMeta.pl
-m coassembly -f fastq_dir -s samples_file -p project_dir

The SQM object structure

The SQM object is a nested list which contains the following information:

If external databases for functional classification were provided to SqueezeMeta via the -extdb argument, the corresponding abundance (reads and bases), coverages, tpm and copy number profiles will be present in SQM$functions (e.g. results for the CAZy database would be present in SQM$functions$CAZy). Additionally, the extended names of the features present in the external database will be present in SQM$misc (e.g. SQM$misc$CAZy_names).

Examples

## Not run: 
## (outside R)
## Run SqueezeMeta on the test data.
 /path/to/SqueezeMeta/scripts/SqueezeMeta.pl -p Hadza -f raw -m coassembly -s test.samples
## Now go into R.
library(SQMtools)
Hadza = loadSQM("Hadza") # Where Hadza is the path to the SqueezeMeta output directory.

## End(Not run)

data(Hadza) # We will illustrate the structure of the SQM object on the test data
# Which are the ten most abundant KEGG IDs in our data?
topKEGG = names(sort(rowSums(Hadza$functions$KEGG$tpm), decreasing=TRUE))[1:11]
topKEGG = topKEGG[topKEGG!="Unclassified"]
# Which functions do those KEGG IDs represent?
Hadza$misc$KEGG_names[topKEGG]
# What is the relative abundance of the Negativicutes class across samples?
Hadza$taxa$class$percent["Negativicutes",]
# Which information is stored in the orf, contig and bin tables?
colnames(Hadza$orfs$table)
colnames(Hadza$contigs$table)
colnames(Hadza$bins$table)
# What is the GC content distribution of my metagenome?
boxplot(Hadza$contigs$table[,"GC perc"]) # Not weighted by contig length or abundance!

Load tables generated by sqm2tables.py, sqmreads2tables.py or combine-sqm-tables.py into R.

Description

This function takes the path to the output directory generated by sqm2tables.py, sqmreads2tables.py or combine-sqm-tables.py a SQMlite object. The SQMlite object will contain taxonomic and functional profiles, but no detailed information on ORFs, contigs or bins. However, it will also have a much smaller memory footprint. A SQMlite object can be used for plotting and exporting, but it can not be subsetted.

Usage

loadSQMlite(tables_path, tax_mode = "allfilter")

Arguments

Value

SQMlite object containing the parsed tables.

The SQMlite object structure

The SQMlite object is a nested list which contains the following information:

If external databases for functional classification were provided to SqueezeMeta or SqueezeMeta_reads via the -extdb argument, the corresponding abundance, tpm and copy number profiles will be present in SQM$functions (e.g. results for the CAZy database would be present in SQM$functions$CAZy). Additionally, the extended names of the features present in the external database will be present in SQM$misc (e.g. SQM$misc$CAZy_names). Note that results generated by SqueezeMeta_reads will contain only read abundances, but not bases, tpm or copy number estimations.

See Also

plotBars and plotFunctions will plot the most abundant taxa and functions in a SQMlite object. exportKrona will generate Krona charts reporting the taxonomy in a SQMlite object.

Examples

## Not run: 
## (outside R)
## Run SqueezeMeta on the test data.
/path/to/SqueezeMeta/scripts/SqueezeMeta.pl -p Hadza -f raw -m coassembly -s test.samples
## Generate the tabular outputs!
/path/to/SqueezeMeta/utils/sqm2tables.py Hadza Hadza/results/tables
## Now go into R.
library(SQMtools)
Hadza = loadSQMlite("Hadza/results/tables")
# Where Hadza is the path to the SqueezeMeta output directory.
# Note that this is not the whole SQM project, just the directory containing the tables.
# It would also work with tables generated by sqmreads2tables.py, or combine-sqm-tables.py
plotTaxonomy(Hadza)
plotFunctions(Hadza)
exportKrona(Hadza, 'myKronaTest.html')

## End(Not run)

Get the N most abundant rows (or columns) from a numeric table

Description

Return a subset of an input matrix or data frame, containing only the N most abundant rows (or columns), sorted. Alternatively, a custom set of rows can be returned.

Usage

mostAbundant(
  data,
  N = 10,
  items = NULL,
  others = FALSE,
  rescale = FALSE,
  bycol = FALSE
)

Arguments

Value

A matrix or data frame (same as input) with the selected rows (or columns).

Examples

data(Hadza)
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Which are the 20 most abundant KEGG functions in the ORFs related to carbohydrate metabolism?
topCarb = mostAbundant(Hadza.carb$functions$KEGG$tpm, N=20)
# Now print them with nice names.
rownames(topCarb) = paste(rownames(topCarb),
                          Hadza.carb$misc$KEGG_names[rownames(topCarb)], sep="; ")
topCarb
# We can pass this to any R function.
heatmap(topCarb)
# But for convenience we provide wrappers for plotting ggplot2 heatmaps and barplots.
plotHeatmap(topCarb, label_y="TPM")
plotBars(topCarb, label_y="TPM")

Get the N most variable rows (or columns) from a numeric table

Description

Return a subset of an input matrix or data frame, containing only the N most variable rows (or columns), sorted. Variability is calculated as the Coefficient of Variation (sd/mean).

Usage

mostVariable(data, N = 10, bycol = FALSE)

Arguments

Value

A matrix or data frame (same as input) with the selected rows or columns.

Examples

data(Hadza)
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Which are the 20 most variable KEGG functions in the ORFs related to carbohydrate metabolism?
topCarb = mostVariable(Hadza.carb$functions$KEGG$tpm, N=20)
# Now print them with nice names
rownames(topCarb) = paste(rownames(topCarb),
                          Hadza.carb$misc$KEGG_names[rownames(topCarb)], sep="; ")
topCarb
# We can pass this to any R function
heatmap(topCarb)
# But for convenience we provide wrappers for plotting ggplot2 heatmaps and barplots
plotHeatmap(topCarb, label_y="TPM")
plotBars(topCarb, label_y="TPM")

Plot a barplot using ggplot2

Description

Plot a ggplot2 barplot from a matrix or data frame. The data should be in tabular format (e.g. features in rows and samples in columns).

Usage

plotBars(
  data,
  label_x = "Samples",
  label_y = "Abundances",
  label_fill = "Features",
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)

Arguments

Value

a ggplot2 plot object.

See Also

plotTaxonomy for plotting the most abundant taxa of a SQM object; plotHeatmap for plotting a heatmap with arbitrary data; mostAbundant for selecting the most abundant rows in a dataframe or matrix.

Examples

data(Hadza)
sk = Hadza$taxa$superkingdom$abund
plotBars(sk, label_y = "Raw reads", label_fill = "Superkingdom")

Barplot of the most abundant bins in a SQM object

Description

This function selects the most abundant bins across all samples in a SQM object and represents their abundances in a barplot. Alternatively, a custom set of bins can be represented.

Usage

plotBins(
  SQM,
  count = "percent",
  N = 15,
  bins = NULL,
  others = TRUE,
  samples = NULL,
  ignore_unmapped = FALSE,
  ignore_nobin = FALSE,
  rescale = FALSE,
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)

Arguments

Value

a ggplot2 plot object.

See Also

plotTaxonomy for plotting the most abundant taxa of a SQM object; plotBars and plotHeatmap for plotting barplots or heatmaps with arbitrary data.

Examples

data(Hadza)
# Bins distribution.
plotBins(Hadza)

Heatmap of the most abundant functions in a SQM object

Description

This function selects the most abundant functions across all samples in a SQM object and represents their abundances in a heatmap. Alternatively, a custom set of functions can be represented.

Usage

plotFunctions(
  SQM,
  fun_level = "KEGG",
  count = "copy_number",
  N = 25,
  fun = NULL,
  samples = NULL,
  display_function_names = TRUE,
  ignore_unmapped = TRUE,
  ignore_unclassified = TRUE,
  gradient_col = c("ghostwhite", "dodgerblue4"),
  rescale_percent = FALSE,
  base_size = 11,
  metadata_groups = NULL
)

Arguments

Value

a ggplot2 plot object.

See Also

plotTaxonomy for plotting the most abundant taxa of a SQM object; plotBars and plotHeatmap for plotting barplots or heatmaps with arbitrary data.

Examples

data(Hadza)
plotFunctions(Hadza)

Plot a heatmap using ggplot2

Description

Plot a ggplot2 heatmap from a matrix or data frame. The data should be in tabular format (e.g. features in rows and samples in columns).

Usage

plotHeatmap(
  data,
  label_x = "Samples",
  label_y = "Features",
  label_fill = "Abundance",
  gradient_col = c("ghostwhite", "dodgerblue4"),
  base_size = 11,
  metadata_groups = NULL
)

Arguments

Value

A ggplot2 plot object.

See Also

plotFunctions for plotting the top functional categories of a SQM object; plotBars for plotting a barplot with arbitrary data; mostAbundant for selecting the most abundant rows in a dataframe or matrix.

Examples

data(Hadza)
topPFAM = mostAbundant(Hadza$functions$PFAM$tpm)
topPFAM = topPFAM[rownames(topPFAM) != "Unclassified",] # Take out the Unclassified ORFs.
plotHeatmap(topPFAM, label_x = "Samples", label_y = "PFAMs", label_fill = "TPM")

Barplot of the most abundant taxa in a SQM object

Description

This function selects the most abundant taxa across all samples in a SQM object and represents their abundances in a barplot. Alternatively, a custom set of taxa can be represented.

Usage

plotTaxonomy(
  SQM,
  rank = "phylum",
  count = "percent",
  N = 15,
  tax = NULL,
  others = TRUE,
  samples = NULL,
  nocds = "treat_separately",
  ignore_unmapped = FALSE,
  ignore_unclassified = FALSE,
  no_partial_classifications = FALSE,
  rescale = FALSE,
  color = NULL,
  base_size = 11,
  max_scale_value = NULL,
  metadata_groups = NULL
)

Arguments

Value

a ggplot2 plot object.

See Also

plotFunctions for plotting the most abundant functions of a SQM object; plotBars and plotHeatmap for plotting barplots or heatmaps with arbitrary data.

Examples

data(Hadza)
Hadza.amin = subsetFun(Hadza, "Amino acid metabolism")
# Taxonomic distribution of amino acid metabolism ORFs at the family level.
plotTaxonomy(Hadza.amin, "family")

Remove contigs from a given bin

Description

Remove contigs from a given bin

Usage

remove_contigs_from_bin(SQM, bin, contigs)

Arguments

Value

SQM object with the new binning information, including recalculated bin statistics if possible.

See Also

find_redundant_contigs, create_bin

Return a vector with the row-wise maxima of a matrix or dataframe.

Description

Return a vector with the row-wise maxima of a matrix or dataframe.

Usage

rowMaxs(table)

Arguments

Value

a vector with the row-wise maxima.

Return a vector with the row-wise minima of a matrix or dataframe.

Description

Return a vector with the row-wise minima of a matrix or dataframe.

Usage

rowMins(table)

Arguments

Value

a vector with the row-wise minima.

Print a named vector of sequences as a fasta-formatted string

Description

Print a named vector of sequences as a fasta-formatted string

Usage

seqvec2fasta(seqvec, output_name = "")

Arguments

Examples

data(Hadza)
seqvec2fasta(Hadza$orfs$seqs[1:10])

Create a SQM object containing only the requested bins, and the contigs and ORFs contained in them.

Description

Create a SQM object containing only the requested bins, and the contigs and ORFs contained in them.

Usage

subsetBins(
  SQM,
  bins,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE,
  allow_empty = FALSE
)

Arguments

Value

SQM object containing only the requested bins.

See Also

subsetContigs, subsetORFs

Examples

data(Hadza)
# Which are the most complete bins?
topBinNames = rownames(Hadza$bins$table)[order(Hadza$bins$table[,"Completeness"],
                                         decreasing=TRUE)][1:2]
# Subset with the most complete bin.
topBin = subsetBins(Hadza, topBinNames[1])

Select contigs

Description

Create a SQM object containing only the requested contigs, the ORFs contained in them and the bins that contain them.

Usage

subsetContigs(
  SQM,
  contigs,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE,
  recalculate_bin_stats = TRUE,
  allow_empty = FALSE
)

Arguments

Value

SQM object containing only the selected contigs.

See Also

subsetORFs

Examples

data(Hadza)
# Which contigs have a GC content below 40?
lowGCcontigNames = rownames(Hadza$contigs$table[Hadza$contigs$table[,"GC perc"]<40,])
lowGCcontigs = subsetContigs(Hadza, lowGCcontigNames)
hist(lowGCcontigs$contigs$table[,"GC perc"])

Filter results by function

Description

Create a SQM or SQMbunch object containing only the ORFs with a given function, and the contigs and bins that contain them.

Usage

subsetFun(
  SQM,
  fun,
  columns = NULL,
  ignore_case = TRUE,
  fixed = FALSE,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE,
  recalculate_bin_stats = FALSE,
  allow_empty = FALSE
)

Arguments

Value

SQM or SQMbunch object containing only the requested function.

See Also

subsetTax, subsetORFs, subsetSamples, combineSQM. The most abundant items of a particular table contained in a SQM object can be selected with mostAbundant.

Examples

data(Hadza)
Hadza.iron = subsetFun(Hadza, "iron")
Hadza.carb = subsetFun(Hadza, "Carbohydrate metabolism")
# Search for multiple patterns using regular expressions
Hadza.twoKOs = subsetFun(Hadza, "K00812|K00813", fixed=FALSE)

Select ORFs

Description

Create a SQM object containing only the requested ORFs, and the contigs and bins that contain them. Internally, all the other subset functions in this package end up calling subsetORFs to do the work for them.

Usage

subsetORFs(
  SQM,
  orfs,
  tax_source = "orfs",
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE,
  recalculate_bin_stats = TRUE,
  contigs_override = NULL,
  allow_empty = FALSE
)

Arguments

Value

SQM object containing the requested ORFs.

A note on contig/bins subsetting

While this function selects the contigs and bins that contain the desired orfs, it DOES NOT recalculate contig abundance and statistics based on the selected ORFs only. This means that the abundances presented in tables such as SQM$contig$abund will still refer to the complete contigs, regardless of whether only a fraction of their ORFs are actually present in the returned SQM object. This is also true for the statistics presented in SQM$contigs$table. Bin statistics may be recalculated if rescale_copy_number is set to TRUE, but recalculation will be based on contigs, not ORFs.

Examples

data(Hadza)
# Select the 100 most abundant ORFs in our dataset.
mostAbundantORFnames = names(sort(rowSums(Hadza$orfs$tpm), decreasing=TRUE))[1:100]
mostAbundantORFs = subsetORFs(Hadza, mostAbundantORFnames)

Select random ORFs

Description

Create a random subset of a SQM object.

Usage

subsetRand(SQM, N)

Arguments

Value

SQM object containing a random subset of ORFs.

See Also

subsetORFs

Filter results by sample

Description

Create a SQM object containing only samples specified by the user, and the ORFs, contigs, bins, taxa and functions present in those samples.

Usage

subsetSamples(SQM, samples, remove_missing = TRUE)

Arguments

Value

SQM object containing only the requested samples.

See Also

subsetTax, subsetFun, subsetORFs, combineSQM. The most abundant items of a particular table contained in a SQM object can be selected with mostAbundant.

Filter results by taxonomy

Description

Create a SQM or SQMbunch object containing only the contigs with a given consensus taxonomy, the ORFs contained in them and the bins that contain them.

Usage

subsetTax(
  SQM,
  rank,
  tax,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = TRUE,
  rescale_copy_number = TRUE,
  recalculate_bin_stats = FALSE,
  allow_empty = FALSE
)

Arguments

Value

SQM or SQMbunch object containing only the requested taxon.

See Also

subsetFun, subsetContigs, subsetSamples, combineSQM. The most abundant items of a particular table contained in a SQM object can be selected with mostAbundant.

Examples

data(Hadza)
Hadza.Prevotella = subsetTax(Hadza, "genus", "Prevotella")
Hadza.Bacteroidota = subsetTax(Hadza, "phylum", "Bacteroidota")

summary method for class SQM

Description

Computes different statistics of the data contained in the SQM object.

Usage

## S3 method for class 'SQM'
summary(object, ...)

Arguments

Value

A list of summary statistics.

summary method for class SQMbunch

Description

Computes different statistics of the data contained in the SQMbunch object.

Usage

## S3 method for class 'SQMbunch'
summary(object, ...)

Arguments

Value

A list of summary statistics.

summary method for class SQMlite

Description

Computes different statistics of the data contained in the SQMlite object.

Usage

## S3 method for class 'SQMlite'
summary(object, ...)

Arguments

Value

A list of summary statistics.